Many users of global positioning system (GPS) devices upload their GPS data (tracks) to the Internet, sometimes in conjunction with photographs and the like, such as for sharing travel and other experiences. In addition to sharing with others, users that upload their GPS tracks may benefit by having a better record of past events, which helps in reliving past events and gaining an understanding of their life patterns. At the same time, applications can attempt to learn from such GPS data, such as to determine popular routes to recommend to others, plan traffic, and so forth.
In general, raw GPS data are browsed and otherwise analyzed directly, without much understanding or context. For example, it would be more useful to applications if users would manually tag or otherwise annotate their GPS tracks with additional information, such as whether they were walking or riding at a particular time. However, there is generally no motivation for users to do so to benefit some unknown application, and further, it is difficult for people to remember the accurate time during a given trip when such additional information is appropriate to include.
Additional data collected by other sensors such as cellular phone devices and towers, Wi-Fi, RFID, and/or other information extracted from geographic maps, such as road networks, may help in interpreting GPS data, but this has its own drawbacks. Some of the drawbacks include a need to have a sufficient number of sensors available and positioned at meaningful locations, the need to correlate such other data with GPS data, the need for users to have cellular phones and/or Wi-Fi devices active and/or possess RFID tags, and so forth. As a result, only raw GPS data is consistently available.
However, given raw GPS data, simple mechanisms cannot accurately infer additional information such as a user's transportation mode. For example, velocity-based rules for determining whether a user is walking or riding fail when traffic conditions and/or weather cause driving velocity to be as slow as walking. When a user takes more than one kind of transportation mode along a trip, the problem becomes more difficult.